{ "_id": "6ab6b46499f7aa8c6aa2f06b", "Package": "xaiHydro", "Type": "Package", "Title": "Explainable AI Tools for Hydro-Climate Modelling", "Version": "0.1.0", "Date": "2026-09-09", "Authors@R": "c(\nperson(\"Sadikul\", \"Islam\",\nemail = \"sadikul.islamiasri@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2924-7122\")),\nperson(\"Shakir\", \"Ali\",\nrole = \"aut\"),\nperson(\"Rajesh\", \"Kaushal\",\nrole = \"aut\"))", "Description": "Provides a unified workflow for applying Explainable\nArtificial Intelligence (XAI) methods to hydro-climate\npredictive models. Functions implement a permutation-based\nMonte Carlo SHAP estimator (Strumbelj and Kononenko (2014)\n; Lundberg and Lee (2017)\n), a self-contained locally\nweighted linear surrogate LIME (Ribeiro et al. (2016)\n), and Partial Dependence Plots\nwith Accumulated Local Effects (Friedman (2001)\n; Apley and Zhu (2020)\n) with hydrology-specific\nvisualisations and interpretation utilities. Supports any model\nobject compatible with the 'DALEX' explainer interface (Biecek\n(2018) ), including random forests,\ngradient boosting, and neural networks trained on streamflow,\ndrought indices, flood risk, or evapotranspiration data.\nHydrology-standard performance metrics Nash-Sutcliffe\nEfficiency (NSE, Nash and Sutcliffe (1970)\n) and Kling-Gupta Efficiency\n(KGE, Gupta et al. (2009) )\nare computed alongside standard regression metrics. Designed to\naccompany the book chapter: Islam, S., Dheeraj, A., Ali, S.,\nKaushal, R. and Venkatesh, G. (2026). Explainable Artificial\nIntelligence for Hydro-Climatic Modelling: Methods,\nApplications, and Implementation Using the xaiHydro R Package.\nIn Chandniha, S. K. et al. (Eds.), Hydro-Climate Analytics:\nRemote Sensing, AI and Geospatial Modelling. 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